Localization of accessory pathways in Wolff-Parkinson-white syndrome using ECG-based multi-task deep learning

Background: Wolff-Parkinson-White syndrome is characterized by accessory atrioventricular pathways (AP) and atrio-ventricular re-entry arrhythmias. Catheter ablation approach and success are determined by AP location. Existing rule-based algorithms based on the electrocardiogram (ECG) are time consuming, prone to inter-observer variability and use delta wave polarity as a binary variable. To overcome these challenges, we propose a model based on a deep neural network (DNN). Methods: Patients with concealed pathways, multiple antegrade conducting pathways or without any sinus rhythm ECGs were excluded. AP location was determined based on electrophysiological testing during catheter ablation and categorized into right-sided, septal and left-sided APs. Multi-task learning with auxiliary identification of the presence of pre-excitation, parahisian pathways and locations where a transseptal puncture is potentially required was used to increase usability and performance. The DNN was compared to the Milstein and Arruda algorithms. Results: Between 1997 and 2023, 645 patients who underwent catheter ablation for an AP were included in the study. The model was developed using 1.394 ECGs from 567 patients. The DNN was tested using 78 ECGs in two independent cohorts. The model outperformed both the Milstein and Arruda algorithms with an area under the receiver operating characteristic curve (AUROC) of.92 (95% confidence interval:.88–.96) compared to the Arruda algorithm (AUROC of.80; p <.001) and the Milstein algorithm (AUROC of.81; p <.001). Conclusions: Our model showed excellent discriminatory performance in predicting the location of an accessory pathway while outperforming conventional techniques. Clinically, this tool can improve preoperative planning and risk stratification.